This paper provides insight into innovation energy, its five working mechanisms, and innovative work behaviour (IWB). Although human energy is often mentioned as an important factor in theories about motivation, it is still an unexplored theme in literature. The management of organisations often focuses on the innovation content and neglects the process aspects. Strategic and operational HRM involvement is needed to realising the essential conditions for the innovation energy of innovative employees. An abductive case study on innovation energy took place in five educational departments of one academy at Saxion University of Applied Sciences in the Netherlands. We interviewed 21 innovating lecturers and their five team leaders individually and organised five focus groups with a total of 17 team members. Innovation energy converts individual innovation properties (creativity, psychological empowerment, and optimism) into IWB. Organisations must pay attention to these properties and four other working mechanisms (autonomy, teamwork, leadership, and external contacts) that influence this conversion process. HRM professionals should be involved with innovation processes to realise the right conditions for innovation energy, together with line management. The construct of innovation energy with five working mechanisms gives more insight into the IWB process from the perspective of the engaged employee with IWB. This research contributes to the body of knowledge on IWB, (human) innovation energy, and engagement in relation to HRM.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

Purpose: Kindergartens are an important educational environment for the development of children at an early age, and they also play a crucial role in developing the values of sustainable development. The purpose of this study is to investigate kindergarten teachers’ perceptions of observable and sustainable development practices. Design, methodology, approach: Semi-structured interviews were conducted with 302 Saudi kindergarten teachers. Additionally, observation cards were utilized to collect data on actual practices of sustainable development in kindergartens. Data were analyzed using Nvivo12, a qualitative data analysis software, and descriptive analysis methods. The main themes were produced first, and then the perspectives were organized around them. Finding: The impact of social and cultural factors on the development of values, the lack of resources available to implement educational activities, and teacher awareness and training gaps were found to be the main barriers to the development of sustainable development values in kindergartens. Originality, value: To the best of the author’s knowledge, this is the first study in Saudi Arabia that has looked into the environmental and social perceptions of early childhood teachers about sustainable development practices, so the study’s findings can highlight the importance of reorienting teacher education programs toward sustainability in order to bridge knowledge and practice gaps.

This study analyses the dynamic development of soybean (Glycine max (L.) Merr.) breeding in Russia, particularly examining its historical development, status, and future predictions. With the global demand for vegetable protein rising, understanding Russia’s potential contribution becomes crucial. This research provides valuable insights, offering precise data that may be unfamiliar to international researchers and the private sector. The authors trace the history of soybean selection in Russia, emphasizing its expansion from the Far East to other regions in Russia. The expansion is primarily attributed to the pioneering work of Soviet breeder V. A. Zolotnitsky and the development of the soybean variety in the Amur region in the 1930s. The study highlights the main areas of soybean variety originators, with approximately 40% of foreign varieties registered. The Krasnodar and Amur regions emerge as critical areas for breeding soybean varieties. In Russia, the highest yield potential of soybeans is in the Central Federal District. At the same time, the varieties registered in the Volga Federal District have higher oil content, and the Far Eastern Federal District has high protein content in the registered soybean varieties. The research outlines the state’s pivotal role in supporting soybean breeding and fostering a competitive market with foreign breeders. The study forecasts future soybean breeding development and the main factors that can influence the industry.

In response to the prevailing energy crisis, this research focuses on elevating the potential of lithium niobate (LN) thin films for advanced optoelectronic applications. Employing electron beam evaporation, films undergo precise annealing (700°C to 1100°C), revealing a structural evolution through X-ray diffraction—crystallite sizes transition from 69.34 nm (unannealed) to 47.90 nm (1100°C). Scanning electron microscopy captures the transformation from coarse grains to photonic crystal clusters, while energy dispersion X-ray analysis discloses LN's composition (97.27 wt.% oxygen, 2.73 wt.% niobium). Rutherford backscattering spectroscopy illustrates surface damage post-Helium ion implantation, proportionate to depth. UV-VIS spectrophotometry highlights a significant blue shift in the optical band gap (3.70 eV to 2.52 eV), with further reduction at 700°C (2.48 eV) and a climactic shift at 1100°C (2.68 eV). This study not only addresses the pressing energy crisis but also emphasizes the indispensable role of lithium niobate in shaping the future of optoelectronics. It provides insights into tailoring LN properties for sustainable advancements in optoelectronic devices, marking a crucial chapter in our collective journey towards energy resilience. The urgency of innovation in the face of global challenges is underscored, marking a crucial chapter in our collective journey towards energy resilience.